Keep generation prompts inside CLIP budget

README.md

@@ -64,6 +64,8 @@ SSD-1B is an Apache-2.0 distilled SDXL model. The LCM adapter enables low-step g
 
 Sprites and backgrounds are generated directly from their written prompts. No procedurally drawn sprite, palette, silhouette, camera guide, or background layout is passed into the production model. Sprite prompts use a portrait canvas and positive single-subject composition language; the negative prompt separately rejects sheets, lineups, repeated subjects, and multiple views. After transparency extraction, sprites with zero or multiple significant foreground components are regenerated with a new seed up to `PRIMARY_SPRITE_ATTEMPTS` times. If every attempt fails, the deployed Space returns an explicit model-generation error rather than returning a contact sheet. Background negative prompts reject characters and generic particle overlays.
 
+SSD-1B's CLIP text encoder has a 77-token context window. Production prompts therefore use a conservative word budget, place the camera and single-subject contract first, and compact the free-form description. This prevents those critical constraints from being silently truncated behind verbose style text.
+
 The deployed Space is fail-closed: if the primary neural model cannot run, generation stops with a visible error instead of silently returning procedural art and calling it model output. The procedural renderer remains available for local development and diagnostics only and cannot influence successful public generations.
 
 ZeroGPU is free for eligible personal accounts, but it is quota-limited rather than unlimited: free accounts currently receive five minutes of GPU time per day. Queueing or a quota message is therefore possible even though no inference credits or payment are required.

app.py

@@ -220,6 +220,28 @@ def camera_prompt_contract(camera: str, is_background: bool) -> str:
     return "Use one internally consistent game camera angle appropriate to the role."
 
 
+def compact_prompt_words(text: str, limit: int) -> str:
+    """Keep model prompts inside CLIP's small token window without tokenizer coupling."""
+    words = re.findall(r"\S+", re.sub(r"\s+", " ", text or "").strip())
+    return " ".join(words[:limit])
+
+
+def primary_camera_phrase(camera: str, is_background: bool) -> str:
+    if camera == "top_down":
+        return "Strict orthographic overhead view directly from above."
+    if camera == "isometric":
+        return "Strict isometric three-quarter projection with parallel axes."
+    if camera == "side_view":
+        return "Strict side-profile view for a 2D platformer."
+    if camera == "first_person":
+        return "Strict first-person viewpoint aligned to the player eye line."
+    if camera == "front_view":
+        return "Strict front-facing view oriented toward the camera."
+    if is_background:
+        return "One consistent playable 2D game camera projection."
+    return "One consistent camera direction for the complete subject."
+
+
 def interpret_style_hint(style_hint: str) -> StylePlan:
     text = (style_hint or "").lower()
     tags: list[str] = []
@@ -1597,13 +1619,6 @@ def diffusion_dimensions(spec: AssetSpec) -> tuple[int, int]:
 
 
 def diffusion_negative_prompt(spec: AssetSpec) -> str:
-    common = (
-        "texture map, material texture, seamless texture, tiled pattern, uv map, uv unwrap, "
-        "3d model texture, normal map, roughness map, diffuse map, sprite sheet, atlas, "
-        "character sheet, model sheet, turnaround, lineup, comparison, alternate views, multiple views, "
-        "duplicate subject, repeated subject, pair, group, multiple objects, cropped subject, close-up surface, "
-        "fabric swatch, text, watermark"
-    )
     if is_background_spec(spec):
         camera_negative = {
             "top_down": ", sky, horizon, eye-level view, front view, front-facing facade, vanishing point",
@@ -1611,15 +1626,15 @@ def diffusion_negative_prompt(spec: AssetSpec) -> str:
             "side_view": ", overhead view, top-down view, isometric view",
         }.get(spec.camera, "")
         return (
-            common
-            + ", person, character, player, hero, creature, monster, vehicle, mascot, foreground subject, "
-            "particle effect, particles, confetti, sparkles, floating dots, glowing orbs, embers, lens flare"
+            "person, character, player, hero, creature, monster, vehicle, mascot, foreground subject, "
+            "particle effect, particles, confetti, sparkles, floating dots, glowing orbs, embers, lens flare, "
+            "texture map, tiled pattern, uv map, text, watermark"
             + camera_negative
         )
     return (
-        common
-        + ", landscape, environment scene, room, horizon, complex background, drop shadow, "
-        "trees, foliage, ground, floor, scenery, background objects, frame, border"
+        "multiple subjects, duplicate character, repeated character, character sheet, model sheet, turnaround, "
+        "lineup, alternate views, multiple poses, multiple views, pair, group, cropped body, scenery, landscape, "
+        "room, trees, foliage, ground, floor, background props, drop shadow, texture map, tiled pattern, text, watermark"
     )
 
 
@@ -1711,18 +1726,18 @@ def initialize_primary_image_model() -> None:
 
 
 def primary_diffusion_prompt(spec: AssetSpec) -> str:
+    camera = primary_camera_phrase(spec.camera, is_background_spec(spec))
     if is_background_spec(spec):
+        description = compact_prompt_words(spec.prompt, 34)
         return (
-            f"{spec.prompt} Generate this scene directly from the written description as a production-quality "
-            "2D game background. Follow the stated camera projection exactly and keep gameplay space readable. "
-            "Use connected environmental forms and clean terrain structure. No characters and no decorative overlay."
+            f"{camera} Empty playable 2D game environment with connected terrain and clear gameplay space. "
+            f"{description}"
         )
+    description = compact_prompt_words(spec.prompt, 24)
+    subject = spec.role.replace("_", " ")
     return (
-        f"{spec.prompt} Production-ready isolated 2D game asset. A single centered subject fills roughly seventy "
-        "percent of a portrait frame. One complete body, one pose, and one strict camera direction are fully visible "
-        "with broad empty margin on every side. Derive the anatomy, body shape, pose, costume, equipment, materials, "
-        "and recognizable details directly from the written description. Use a plain uniform white studio field, a "
-        "readable silhouette, and a clean unlabeled presentation."
+        f"{camera} Single isolated {subject} subject. One complete body, one pose, centered and fully visible with "
+        f"broad empty margins on a uniform white field. {description}"
     )
 
 
@@ -2301,6 +2316,7 @@ def model_configuration_summary() -> str:
         f"remote image fallback: `{remote_fallback}` · neural image models: `{neural_status}` · "
         f"neural enforcement: `{enforcement}` · primary readiness: `{readiness}`. "
         "Every production image is generated directly from its written prompt by the primary text-to-image model. "
+        "Critical camera and subject constraints are placed first inside a conservative CLIP prompt budget. "
         "Sprite outputs must pass single-subject alpha-component validation and are regenerated with a new seed up to "
         f"{PRIMARY_SPRITE_ATTEMPTS} times. No procedural guide is supplied to the model. The procedural renderer is "
         "development-only and is blocked in the deployed Space when the primary model fails."